Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations54000
Missing cells29
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 MiB
Average record size in memory120.0 B

Variable types

Text2
DateTime2
Numeric7
Categorical4

Alerts

DaysWorkedPerWeek is highly overall correlated with PartTimeFullTimeHigh correlation
InitialIncurredClaimsCost is highly overall correlated with UltimateIncurredClaimCostHigh correlation
PartTimeFullTime is highly overall correlated with DaysWorkedPerWeekHigh correlation
UltimateIncurredClaimCost is highly overall correlated with InitialIncurredClaimsCostHigh correlation
Gender is highly imbalanced (51.0%)Imbalance
DependentsOther is highly imbalanced (96.6%)Imbalance
PartTimeFullTime is highly imbalanced (56.2%)Imbalance
HoursWorkedPerWeek is highly skewed (γ1 = 24.13297421)Skewed
InitialIncurredClaimsCost is highly skewed (γ1 = 26.85365748)Skewed
UltimateIncurredClaimCost is highly skewed (γ1 = 37.55250381)Skewed
ClaimNumber has unique valuesUnique
DependentChildren has 50639 (93.8%) zerosZeros

Reproduction

Analysis started2024-08-13 14:23:38.568146
Analysis finished2024-08-13 14:23:50.814184
Duration12.25 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

ClaimNumber
Text

UNIQUE 

Distinct54000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:51.148834image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters486000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54000 ?
Unique (%)100.0%

Sample

1st rowWC8285054
2nd rowWC6982224
3rd rowWC5481426
4th rowWC9775968
5th rowWC2634037
ValueCountFrequency (%)
wc8285054 1
 
< 0.1%
wc3735596 1
 
< 0.1%
wc6049270 1
 
< 0.1%
wc8595173 1
 
< 0.1%
wc2826510 1
 
< 0.1%
wc5481426 1
 
< 0.1%
wc9775968 1
 
< 0.1%
wc2634037 1
 
< 0.1%
wc6828422 1
 
< 0.1%
wc8058150 1
 
< 0.1%
Other values (53990) 53990
> 99.9%
2024-08-13T09:23:51.710854image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 54000
11.1%
C 54000
11.1%
9 39592
8.1%
7 39516
8.1%
8 39067
8.0%
6 38929
8.0%
3 38648
8.0%
5 38563
7.9%
4 38214
7.9%
2 38174
7.9%
Other values (2) 67297
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 486000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 54000
11.1%
C 54000
11.1%
9 39592
8.1%
7 39516
8.1%
8 39067
8.0%
6 38929
8.0%
3 38648
8.0%
5 38563
7.9%
4 38214
7.9%
2 38174
7.9%
Other values (2) 67297
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 486000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 54000
11.1%
C 54000
11.1%
9 39592
8.1%
7 39516
8.1%
8 39067
8.0%
6 38929
8.0%
3 38648
8.0%
5 38563
7.9%
4 38214
7.9%
2 38174
7.9%
Other values (2) 67297
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 486000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 54000
11.1%
C 54000
11.1%
9 39592
8.1%
7 39516
8.1%
8 39067
8.0%
6 38929
8.0%
3 38648
8.0%
5 38563
7.9%
4 38214
7.9%
2 38174
7.9%
Other values (2) 67297
13.8%
Distinct36673
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
Minimum1988-01-01 09:00:00+00:00
Maximum2005-12-31 10:00:00+00:00
2024-08-13T09:23:51.928319image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:52.114600image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct6653
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
Minimum1988-01-08 00:00:00+00:00
Maximum2006-09-23 00:00:00+00:00
2024-08-13T09:23:52.296232image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:52.482347image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Age
Real number (ℝ)

Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.84237
Minimum13
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:52.692972image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18
Q123
median32
Q343
95-th percentile56
Maximum81
Range68
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.122165
Coefficient of variation (CV)0.3581949
Kurtosis-0.60607452
Mean33.84237
Median Absolute Deviation (MAD)9
Skewness0.53634113
Sum1827488
Variance146.94687
MonotonicityNot monotonic
2024-08-13T09:23:52.886630image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 2108
 
3.9%
21 2023
 
3.7%
20 1987
 
3.7%
22 1976
 
3.7%
23 1962
 
3.6%
25 1758
 
3.3%
24 1756
 
3.3%
18 1736
 
3.2%
27 1660
 
3.1%
28 1625
 
3.0%
Other values (58) 35409
65.6%
ValueCountFrequency (%)
13 9
 
< 0.1%
14 34
 
0.1%
15 158
 
0.3%
16 519
 
1.0%
17 1058
2.0%
18 1736
3.2%
19 2108
3.9%
20 1987
3.7%
21 2023
3.7%
22 1976
3.7%
ValueCountFrequency (%)
81 1
 
< 0.1%
80 1
 
< 0.1%
79 2
 
< 0.1%
78 2
 
< 0.1%
76 4
 
< 0.1%
75 5
< 0.1%
74 7
< 0.1%
73 1
 
< 0.1%
72 11
< 0.1%
71 7
< 0.1%

Gender
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
M
41660 
F
12338 
U
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 41660
77.1%
F 12338
 
22.8%
U 2
 
< 0.1%

Length

2024-08-13T09:23:53.064872image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T09:23:53.241431image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
m 41660
77.1%
f 12338
 
22.8%
u 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 41660
77.1%
F 12338
 
22.8%
U 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 41660
77.1%
F 12338
 
22.8%
U 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 41660
77.1%
F 12338
 
22.8%
U 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 41660
77.1%
F 12338
 
22.8%
U 2
 
< 0.1%

MaritalStatus
Categorical

Distinct3
Distinct (%)< 0.1%
Missing29
Missing (%)0.1%
Memory size422.0 KiB
S
26161 
M
22516 
U
5294 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters53971
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowU
4th rowS
5th rowM

Common Values

ValueCountFrequency (%)
S 26161
48.4%
M 22516
41.7%
U 5294
 
9.8%
(Missing) 29
 
0.1%

Length

2024-08-13T09:23:53.384320image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T09:23:53.515392image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
s 26161
48.5%
m 22516
41.7%
u 5294
 
9.8%

Most occurring characters

ValueCountFrequency (%)
S 26161
48.5%
M 22516
41.7%
U 5294
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 26161
48.5%
M 22516
41.7%
U 5294
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 26161
48.5%
M 22516
41.7%
U 5294
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 26161
48.5%
M 22516
41.7%
U 5294
 
9.8%

DependentChildren
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11918519
Minimum0
Maximum9
Zeros50639
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:53.644951image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51778002
Coefficient of variation (CV)4.3443321
Kurtosis30.006216
Mean0.11918519
Median Absolute Deviation (MAD)0
Skewness5.1123793
Sum6436
Variance0.26809615
MonotonicityNot monotonic
2024-08-13T09:23:53.778454image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 50639
93.8%
2 1361
 
2.5%
1 1273
 
2.4%
3 528
 
1.0%
4 150
 
0.3%
5 42
 
0.1%
6 5
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 50639
93.8%
1 1273
 
2.4%
2 1361
 
2.5%
3 528
 
1.0%
4 150
 
0.3%
5 42
 
0.1%
6 5
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
6 5
 
< 0.1%
5 42
 
0.1%
4 150
 
0.3%
3 528
 
1.0%
2 1361
 
2.5%
1 1273
 
2.4%
0 50639
93.8%

DependentsOther
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
0
53506 
1
 
462
2
 
23
3
 
8
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 53506
99.1%
1 462
 
0.9%
2 23
 
< 0.1%
3 8
 
< 0.1%
5 1
 
< 0.1%

Length

2024-08-13T09:23:53.926538image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T09:23:54.062064image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 53506
99.1%
1 462
 
0.9%
2 23
 
< 0.1%
3 8
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 53506
99.1%
1 462
 
0.9%
2 23
 
< 0.1%
3 8
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 53506
99.1%
1 462
 
0.9%
2 23
 
< 0.1%
3 8
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 53506
99.1%
1 462
 
0.9%
2 23
 
< 0.1%
3 8
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 53506
99.1%
1 462
 
0.9%
2 23
 
< 0.1%
3 8
 
< 0.1%
5 1
 
< 0.1%

WeeklyWages
Real number (ℝ)

Distinct13211
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.36481
Minimum1
Maximum7497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:54.223867image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile200
Q1200
median392.2
Q3500
95-th percentile817.0055
Maximum7497
Range7496
Interquartile range (IQR)300

Descriptive statistics

Standard deviation248.63867
Coefficient of variation (CV)0.59716543
Kurtosis68.023352
Mean416.36481
Median Absolute Deviation (MAD)152.2
Skewness4.1227669
Sum22483700
Variance61821.188
MonotonicityNot monotonic
2024-08-13T09:23:54.420909image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 12372
 
22.9%
500 4271
 
7.9%
300 570
 
1.1%
400 389
 
0.7%
350 336
 
0.6%
600 294
 
0.5%
450 193
 
0.4%
250 186
 
0.3%
289.93 145
 
0.3%
480 127
 
0.2%
Other values (13201) 35117
65.0%
ValueCountFrequency (%)
1 122
0.2%
1.91 1
 
< 0.1%
3.59 1
 
< 0.1%
3.95 2
 
< 0.1%
4.61 1
 
< 0.1%
4.73 2
 
< 0.1%
5 16
 
< 0.1%
5.25 2
 
< 0.1%
5.49 2
 
< 0.1%
5.78 1
 
< 0.1%
ValueCountFrequency (%)
7497 3
< 0.1%
7400 1
 
< 0.1%
6453 1
 
< 0.1%
4556 2
< 0.1%
4311.3 1
 
< 0.1%
3750 4
< 0.1%
3500 2
< 0.1%
2956.52 2
< 0.1%
2817.92 1
 
< 0.1%
2766.04 2
< 0.1%

PartTimeFullTime
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
F
49112 
P
 
4888

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 49112
90.9%
P 4888
 
9.1%

Length

2024-08-13T09:23:54.597182image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T09:23:54.723288image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
f 49112
90.9%
p 4888
 
9.1%

Most occurring characters

ValueCountFrequency (%)
F 49112
90.9%
P 4888
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 49112
90.9%
P 4888
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 49112
90.9%
P 4888
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 49112
90.9%
P 4888
 
9.1%

HoursWorkedPerWeek
Real number (ℝ)

SKEWED 

Distinct424
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.735084
Minimum0
Maximum640
Zeros29
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:54.871596image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.3815
Q138
median38
Q340
95-th percentile40
Maximum640
Range640
Interquartile range (IQR)2

Descriptive statistics

Standard deviation12.568704
Coefficient of variation (CV)0.3330774
Kurtosis910.21194
Mean37.735084
Median Absolute Deviation (MAD)0
Skewness24.132974
Sum2037694.6
Variance157.97231
MonotonicityNot monotonic
2024-08-13T09:23:55.067013image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 30829
57.1%
40 13283
24.6%
20 894
 
1.7%
30 837
 
1.6%
35 743
 
1.4%
37.5 663
 
1.2%
25 414
 
0.8%
50 322
 
0.6%
15 301
 
0.6%
45 284
 
0.5%
Other values (414) 5430
 
10.1%
ValueCountFrequency (%)
0 29
0.1%
1 31
0.1%
2 6
 
< 0.1%
2.1 1
 
< 0.1%
3 26
< 0.1%
3.5 5
 
< 0.1%
4 34
0.1%
4.1 1
 
< 0.1%
4.5 4
 
< 0.1%
5 50
0.1%
ValueCountFrequency (%)
640 1
 
< 0.1%
638 2
< 0.1%
627 2
< 0.1%
538.3 2
< 0.1%
462.08 2
< 0.1%
450 3
< 0.1%
417.2 1
 
< 0.1%
410 2
< 0.1%
400 3
< 0.1%
389 3
< 0.1%

DaysWorkedPerWeek
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9057593
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:55.220596image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q35
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55212911
Coefficient of variation (CV)0.11254713
Kurtosis18.240675
Mean4.9057593
Median Absolute Deviation (MAD)0
Skewness-3.3404679
Sum264911
Variance0.30484655
MonotonicityNot monotonic
2024-08-13T09:23:55.354634image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 49185
91.1%
4 1476
 
2.7%
3 1436
 
2.7%
6 884
 
1.6%
2 513
 
0.9%
7 323
 
0.6%
1 183
 
0.3%
ValueCountFrequency (%)
1 183
 
0.3%
2 513
 
0.9%
3 1436
 
2.7%
4 1476
 
2.7%
5 49185
91.1%
6 884
 
1.6%
7 323
 
0.6%
ValueCountFrequency (%)
7 323
 
0.6%
6 884
 
1.6%
5 49185
91.1%
4 1476
 
2.7%
3 1436
 
2.7%
2 513
 
0.9%
1 183
 
0.3%
Distinct28114
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:55.772727image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length94
Median length74
Mean length43.453704
Min length3

Characters and Unicode

Total characters2346500
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21436 ?
Unique (%)39.7%

Sample

1st rowLIFTING TYRE INJURY TO RIGHT ARM AND WRIST INJURY
2nd rowSTEPPED AROUND CRATES AND TRUCK TRAY FRACTURE LEFT FOREARM
3rd rowCUT ON SHARP EDGE CUT LEFT THUMB
4th rowDIGGING LOWER BACK LOWER BACK STRAIN
5th rowREACHING ABOVE SHOULDER LEVEL ACUTE MUSCLE STRAIN LEFT SIDE OF STOMACH
ValueCountFrequency (%)
right 22648
 
6.0%
left 20756
 
5.5%
back 16346
 
4.3%
strain 15259
 
4.0%
lower 9950
 
2.6%
and 9103
 
2.4%
finger 8584
 
2.3%
lifting 8300
 
2.2%
hand 7723
 
2.0%
struck 7354
 
1.9%
Other values (3718) 253028
66.8%
2024-08-13T09:23:56.734486image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
325051
13.9%
E 216976
 
9.2%
I 176650
 
7.5%
T 176030
 
7.5%
R 171992
 
7.3%
N 153319
 
6.5%
A 138288
 
5.9%
L 127791
 
5.4%
S 99153
 
4.2%
O 94967
 
4.0%
Other values (19) 666283
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2346500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
325051
13.9%
E 216976
 
9.2%
I 176650
 
7.5%
T 176030
 
7.5%
R 171992
 
7.3%
N 153319
 
6.5%
A 138288
 
5.9%
L 127791
 
5.4%
S 99153
 
4.2%
O 94967
 
4.0%
Other values (19) 666283
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2346500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
325051
13.9%
E 216976
 
9.2%
I 176650
 
7.5%
T 176030
 
7.5%
R 171992
 
7.3%
N 153319
 
6.5%
A 138288
 
5.9%
L 127791
 
5.4%
S 99153
 
4.2%
O 94967
 
4.0%
Other values (19) 666283
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2346500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
325051
13.9%
E 216976
 
9.2%
I 176650
 
7.5%
T 176030
 
7.5%
R 171992
 
7.3%
N 153319
 
6.5%
A 138288
 
5.9%
L 127791
 
5.4%
S 99153
 
4.2%
O 94967
 
4.0%
Other values (19) 666283
28.4%

InitialIncurredClaimsCost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1989
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7841.146
Minimum1
Maximum2000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:56.941617image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile315
Q1700
median2000
Q39500
95-th percentile30000
Maximum2000000
Range1999999
Interquartile range (IQR)8800

Descriptive statistics

Standard deviation20584.075
Coefficient of variation (CV)2.625136
Kurtosis1888.277
Mean7841.146
Median Absolute Deviation (MAD)1500
Skewness26.853657
Sum4.2342188 × 108
Variance4.2370414 × 108
MonotonicityNot monotonic
2024-08-13T09:23:57.151849image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 6260
 
11.6%
1000 4587
 
8.5%
10000 3453
 
6.4%
3500 2600
 
4.8%
7500 2507
 
4.6%
1500 2329
 
4.3%
2000 1607
 
3.0%
9500 1255
 
2.3%
5000 928
 
1.7%
25000 831
 
1.5%
Other values (1979) 27643
51.2%
ValueCountFrequency (%)
1 46
0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
50 8
 
< 0.1%
55 1
 
< 0.1%
60 4
 
< 0.1%
70 6
 
< 0.1%
ValueCountFrequency (%)
2000000 1
< 0.1%
872980 1
< 0.1%
830000 2
< 0.1%
725000 1
< 0.1%
690000 1
< 0.1%
540000 1
< 0.1%
500000 1
< 0.1%
450000 1
< 0.1%
425000 2
< 0.1%
421000 1
< 0.1%

UltimateIncurredClaimCost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct53999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11003.369
Minimum121.88681
Maximum4027135.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.0 KiB
2024-08-13T09:23:57.358512image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum121.88681
5-th percentile306.55693
Q1926.33845
median3371.2417
Q38197.2486
95-th percentile45224.184
Maximum4027135.9
Range4027014
Interquartile range (IQR)7270.9102

Descriptive statistics

Standard deviation33390.991
Coefficient of variation (CV)3.0346152
Kurtosis3940.8638
Mean11003.369
Median Absolute Deviation (MAD)2786.0995
Skewness37.552504
Sum5.9418194 × 108
Variance1.1149583 × 109
MonotonicityNot monotonic
2024-08-13T09:23:57.570049image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1092.841276 2
 
< 0.1%
4748.203388 1
 
< 0.1%
3979.777262 1
 
< 0.1%
29888.15421 1
 
< 0.1%
752.4568306 1
 
< 0.1%
285.4824447 1
 
< 0.1%
2033.28861 1
 
< 0.1%
18299.90692 1
 
< 0.1%
1839.359986 1
 
< 0.1%
6285.121747 1
 
< 0.1%
Other values (53989) 53989
> 99.9%
ValueCountFrequency (%)
121.8868054 1
< 0.1%
123.1648797 1
< 0.1%
124.5796609 1
< 0.1%
129.1061303 1
< 0.1%
131.457013 1
< 0.1%
132.9995569 1
< 0.1%
134.3203499 1
< 0.1%
138.4751978 1
< 0.1%
139.8539663 1
< 0.1%
140.1828009 1
< 0.1%
ValueCountFrequency (%)
4027135.935 1
< 0.1%
865770.6486 1
< 0.1%
823706.3012 1
< 0.1%
768485.1182 1
< 0.1%
742003.2335 1
< 0.1%
741498.0275 1
< 0.1%
713784.0636 1
< 0.1%
608650.4259 1
< 0.1%
586912.8191 1
< 0.1%
558408.9616 1
< 0.1%

Interactions

2024-08-13T09:23:48.731398image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:42.149350image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.375280image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.357086image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:45.405247image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.631759image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.647620image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:48.885834image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:42.319608image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.515370image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.506981image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:45.550699image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.778308image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.803453image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:49.027432image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:42.610727image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.639777image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.640541image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:45.880538image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.907620image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.943253image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:49.183133image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:42.758056image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.780507image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.790747image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.029832image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.054686image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:48.100147image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:49.335093image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:42.904873image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.920183image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.937348image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.172559image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.196293image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:48.252352image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:49.483952image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.054848image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.056188image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:45.083747image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.315364image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.335679image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:48.404197image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:49.649857image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:43.213843image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:44.207395image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:45.244427image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:46.474477image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:47.490981image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-13T09:23:48.565254image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-13T09:23:57.719718image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
AgeDaysWorkedPerWeekDependentChildrenDependentsOtherGenderHoursWorkedPerWeekInitialIncurredClaimsCostMaritalStatusPartTimeFullTimeUltimateIncurredClaimCostWeeklyWages
Age1.0000.0320.0970.0240.0650.0270.1960.4050.0900.2340.210
DaysWorkedPerWeek0.0321.0000.0190.0080.1410.429-0.0020.0330.712-0.0050.172
DependentChildren0.0970.0191.0000.1190.0010.0560.0370.1610.0190.0570.109
DependentsOther0.0240.0080.1191.0000.0100.0000.0240.0570.0000.0000.023
Gender0.0650.1410.0010.0101.0000.0100.0000.0230.2490.0000.051
HoursWorkedPerWeek0.0270.4290.0560.0000.0101.0000.0140.0050.0140.0260.287
InitialIncurredClaimsCost0.196-0.0020.0370.0240.0000.0141.0000.0090.0010.8830.301
MaritalStatus0.4050.0330.1610.0570.0230.0050.0091.0000.0220.0000.053
PartTimeFullTime0.0900.7120.0190.0000.2490.0140.0010.0221.0000.0000.064
UltimateIncurredClaimCost0.234-0.0050.0570.0000.0000.0260.8830.0000.0001.0000.350
WeeklyWages0.2100.1720.1090.0230.0510.2870.3010.0530.0640.3501.000

Missing values

2024-08-13T09:23:50.137582image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T09:23:50.546042image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ClaimNumberDateTimeOfAccidentDateReportedAgeGenderMaritalStatusDependentChildrenDependentsOtherWeeklyWagesPartTimeFullTimeHoursWorkedPerWeekDaysWorkedPerWeekClaimDescriptionInitialIncurredClaimsCostUltimateIncurredClaimCost
0WC82850542002-04-09T07:00:00Z2002-07-05T00:00:00Z48MM00500.00F38.05LIFTING TYRE INJURY TO RIGHT ARM AND WRIST INJURY15004748.203388
1WC69822241999-01-07T11:00:00Z1999-01-20T00:00:00Z43FM00509.34F37.55STEPPED AROUND CRATES AND TRUCK TRAY FRACTURE LEFT FOREARM55006326.285819
2WC54814261996-03-25T00:00:00Z1996-04-14T00:00:00Z30MU00709.10F38.05CUT ON SHARP EDGE CUT LEFT THUMB17002293.949087
3WC97759682005-06-22T13:00:00Z2005-07-22T00:00:00Z41MS00555.46F38.05DIGGING LOWER BACK LOWER BACK STRAIN1500017786.487170
4WC26340371990-08-29T08:00:00Z1990-09-27T00:00:00Z36MM00377.10F38.05REACHING ABOVE SHOULDER LEVEL ACUTE MUSCLE STRAIN LEFT SIDE OF STOMACH28004014.002925
5WC68284221999-06-21T11:00:00Z1999-09-09T00:00:00Z50MM00200.00F38.05STRUCK HEAD ON HEAD LACERATED HEAD500598.762315
6WC80581502001-07-13T11:00:00Z2001-07-23T00:00:00Z39MM00200.00F38.05FINGER BRUISED AND SWOLLEN LEFT ARM500279.068178
7WC75398492000-03-09T09:00:00Z2000-04-16T00:00:00Z56MM00200.00F40.05CLEANING LEFT SHOULDER SPLINTER LEFT HAND5001877.172243
8WC44271791994-03-24T16:00:00Z1994-04-26T00:00:00Z49MM00623.60F38.05JACK SLIPPED CATCHING FINGER CUT LEFT LITTLE FINGER9251254.129811
9WC99076362005-12-07T11:00:00Z2005-12-22T00:00:00Z30MS00857.28F37.05STRUCK PINE DUST ABRASION LEFT EYE IRRITATION15001031.603044
ClaimNumberDateTimeOfAccidentDateReportedAgeGenderMaritalStatusDependentChildrenDependentsOtherWeeklyWagesPartTimeFullTimeHoursWorkedPerWeekDaysWorkedPerWeekClaimDescriptionInitialIncurredClaimsCostUltimateIncurredClaimCost
53990WC34854031992-03-04T11:00:00Z1992-03-19T00:00:00Z32MM00384.03F38.05LIFTING DOOR INJURED SHOULDER AND LEFT FOREARM LEFT HIP12002633.844395
53991WC74262192000-08-28T15:00:00Z2000-11-16T00:00:00Z21MS00451.83F38.05SLICING VEGETABLES LACERATION RIGHT INDEX FINGER LACERATION10001248.103245
53992WC62630251997-04-18T11:00:00Z1997-04-28T00:00:00Z21MS00200.00F38.05STRUCK HAND WITH ALLEN KEY LACERATION LEFT HAND500233.289431
53993WC44471561994-07-07T18:00:00Z1994-10-01T00:00:00Z47MM00532.00F38.05FELL FLOOR MAT STRAIN LOWER BACK AND NECK100008196.288506
53994WC70065071999-07-19T11:00:00Z2001-03-04T00:00:00Z35MM00200.00F40.05FELL STAIRS BRUISE RIGHT ANKLE AND RIGHT LEG1500011847.081780
53995WC93707272004-08-21T18:00:00Z2004-09-08T00:00:00Z32FS00500.00F38.05STRUCK KNIFE LACERATED LEFT MIDDLE FINGER LEFT HAND1000480.493308
53996WC83962692002-04-28T09:00:00Z2002-09-03T00:00:00Z20FS00500.00F40.05LEFT HAND LACERATION LEFT SIDE BACK AND LEFT LEG1000755.735319
53997WC36095281992-02-28T09:00:00Z1992-03-18T00:00:00Z19MS00283.00F40.05METAL SLIPPED ACROSS METAL CUT FINGER210418.178461
53998WC50385651995-01-10T07:00:00Z1995-01-31T00:00:00Z24MS00200.00F38.05BURN WHILST USING SPANNER LACERATION RIGHT MIDDLE FINGER75002695.225700
53999WC25426011990-10-24T14:00:00Z1990-11-03T00:00:00Z22MS00200.00F38.05CUT WITH BREAD KNIFE LACERATION LEFT INDEX AND MIDDLE FINGERS550934.273548